基于人工免疫算法和蚁群算法求解旅行商问题

人工免疫算法具有快速随机的全局搜索能力,但对于系统中的反馈信息利用不足,往往做大量无为的冗余迭代,求解效率低。蚁群算法具有分布式并行全局搜索能力,通过信息素的积累和更新收敛于最优路径上,但初期信息素匮乏,求解速度慢。该文提出一种基于人工免疫算法和蚁群算法的混合算法,采用人工免疫算法生成信息素分布,利用蚁群算法求优化解。将该算法用于求解旅行商问题进行计算机仿真,结果表明,该算法是一种收敛速度和寻优能力都比较好的优化方法。

A Hybrid Algorithm Based on Artificial Immune Algorithm and Ant Colony Algorithm for Solving Traveling Salesman Problem

Artificial Immune Algorithm has the ability of doing a global searching quickly and stochastically.But it can't make use of enough system output information,and hence has to do a large redundancy repeat searching for the optimal solution,which reduces the efficiency of algorithm.Ant Colony Algorithm converges on the optimal path through pheromone accumulation and renewal,and has the ability of parallel processing and global searching.But its convergence speed is slow because of poor pheromone on the path early.In this paper we propose a hybrid algorithm based on Artificial Immune Algorithm and Ant Colony Algorithm.It adopts Artificial Immune Algorithm to give pheromone to distribute and makes use of Ant Colony Algorithm to give the optimal solution.The computer simulation results show that the proposed algorithm is better than the previous two algorithms on the convergence speed and ability of searching for approximate global optimal solution for solving Traveling Salesman Problem.